ryanwitt

Keybase proof

I hereby claim:

• I am ryanwitt on github.
• I am onecreativenerd (https://keybase.io/onecreativenerd) on keybase.
• I have a public key ASB_TkeaXoaqMw5ii1FGzwCwYblooenmt-s59k24W87OZAo

To claim this, I am signing this object:

ryanwitt

class RedisTools:
    '''
    A set of utility tools for interacting with a redis cache
    '''

    def __init__(self):
        self._queues = ["default", "high", "low", "failed"]
        self.get_redis_connection()

    def get_redis_connection(self):

ryanwitt

#!/bin/sh

VERSION=0.12.2
PLATFORM=linux
ARCH=x64
PREFIX=/usr/local

mkdir -p "$PREFIX" && \
curl http://nodejs.org/dist/v$VERSION/node-v$VERSION-$PLATFORM-$ARCH.tar.gz \
  | tar xzvf - --strip-components=1 -C "$PREFIX"

ryanwitt

def collect_ranges(s):
    """
    Returns a generator of tuples of consecutive numbers found in the input.

    >>> list(collect_ranges([]))
    []
    >>> list(collect_ranges([1]))
    [(1, 1)]
    >>> list(collect_ranges([1,2,3]))
    [(1, 3)]

ryanwitt

//
// cpuse.js - simple continuous cpu monitor for node
//
// Intended for programs wanting to monitor and take action on overall CPU load.
//
// The monitor starts as soon as you require the module, then you can query it at
// any later time for the average cpu:
//
// > var cpuse = require('cpuse');
// > cpuse.averages();

ryanwitt

// Check mongodb working set size (Mongo 2.4+). 
// Paste this into mongo console, get back size in GB

db.runCommand({
  serverStatus:1, workingSet:1, metrics:0, locks:0
}).workingSet.pagesInMemory * 4096 / (Math.pow(2,30));

ryanwitt

ryanwitt

Doctor referral graph / NPI database full-text indexer

You need 7zip installed to grab the NPI database. (brew install p7zip osx)

To create the index, run the init_* scripts. You would need the doctor graph referral data to use *_refer.*, but the NPI database will be automatically downloaded for you. Indexing happens on all cores, and takes less than 10 min on my 8 core machine.

To grab lines matching a search term, use python search_npi.py term.

Note: index performance is good if you have a lot of memory. Index file blocks will stay hot in cache, but they are loaded each time the program is run, which is super inefficient. Should use an on-disk hashtable where the offsets can be calculated instead.

ryanwitt

froms = {}
tos = {}
for i,line in enumerate(file('refer.2011.csv')):
    try:
        fr, to, count = line.strip().split(',')
        froms[fr] = froms.get(fr,0) + 1
        tos[to] = tos.get(to,0) + 1
    except:
        import traceback; traceback.print_exc()

ryanwitt

import random
import matplotlib.pyplot as plt

k = 1000
array = []
for n, x in enumerate([range(k)[random.randrange(k)] for x in range(100000)]):
    if n < k:
        array.append(x)
    else:
        if random.random() < k/float(n):